In this paper, a new algorithm for Chinese character recognition is presented. This method has an especial effect on character images those are not clear. Comparing with a clear character image, a blurred character image always has a big change in shape that makes the recognition more difficult. In order to recognize blurred characters precisely, the concept of degree of blur is employed. We also investigate how the distribution of feature vectors changes if a part of character image is crashed. Since the degree of blur is calculated from each sub-area of character image, the condition of the image can be described clearly. The distribution can be reconstructed to respond to the change expressed by the degree of blur. The method proposed in this paper can recognize blurred characters precisely by modifying the Mahalanobis distance function according to change in distribution. The effectiveness of the new method is shown by experiments with blurred character images.